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Register a new userX-ray imaging detector for radiological applications in the harsh environments of low-income countries
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Added by
Klaus Sch\\\"onenberger
Publication date
2020
fields
Physics
and research's language is
English
Authors
Mario A. Chavarria
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This paper describes the development of a novel medical Xray imaging system adapted to the needs and constraints of low and middle income countries. The developed system is based on an indirect conversion chain: a scintillator plate produces visible light when excited by the Xrays, then a calibrated multi camera architecture converts the visible light from the scintillator into a set of digital images. The partial images are then unwarped, enhanced and stitched through parallel processing units and a specialized software. All the detector components were carefully selected focusing on optimizing the system s image quality, robustness, cost, effectiveness and capability to work in harsh tropical environments. With this aim, different customized and commercial components were characterized. The resulting detector can generate high quality medical diagnostic images with DQE levels up to 60 percent, at 2.34 micro Gray, even under harsh environments i.e. 60 degrees Celsius and 98 percent humidity.
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The use of x-ray imaging in medicine and other research is well known. Generally, the image quality is proportional to the total flux, but high photon energy could severely damage the specimen, so how to decrease the radiation dose while maintaining image quality is a fundamental problem. In ghost imaging, an image is retrieved from a known patterned illumination field and the total intensity transmitted through the object collected by a bucket detector. Using a table-top x-ray source we have realized ghost imaging of plane and natural objects with ultra-low radiation on the order of single photons. Compared with conventional x-ray imaging, a higher contrast-to-noise ratio is obtained for the same radiation dose. This new technique could greatly reduce radiation damage of biological specimens.
Conventional energy filters for x-ray imaging are based on absorbing materials which attenuate low energy photons, sometimes combined with an absorption edge, thus also discriminating towards photons of higher energies. These filters are fairly inefficient, in particular for photons of higher energies, and other methods for achieving a narrower bandwidth have been proposed. Such methods include various types of monochromators, based on for instance mosaic crystals or refractive multi-prism x-ray lenses (MPLs). Prism-array lenses (PALs) are similar to MPLs, but are shorter, have larger apertures, and higher transmission. A PAL consists of a number of small prisms arranged in columns perpendicular to the optical axis. The column height decreases along the optical axis so that the projection of lens material is approximately linear with a Fresnel phase-plate pattern superimposed on it. The focusing effect is one dimensional, and the lens is chromatic. Hence, unwanted energies can be blocked by placing a slit in the image plane of a desired energy. We present the first experimental and theoretical results on an energy filter based on a silicon PAL. The study includes an evaluation of the spectral shaping properties of the filter as well as a quantification of the achievable increase in dose efficiency compared to standard methods. Previously, PALs have been investigated with synchrotron radiation, but in this study a medical imaging setup, based on a regular x-ray tube, is considered.
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In this paper, we present a new method to generate an instantaneous volumetric image using a single x-ray projection. To fully extract motion information hidden in projection images, we partitioned a projection image into small patches. We utilized a sparse learning method to automatically select patches that have a high correlation with principal component analysis (PCA) coefficients of a lung motion model. A model that maps the patch intensity to the PCA coefficients is built along with the patch selection process. Based on this model, a measured projection can be used to predict the PCA coefficients, which are further used to generate a motion vector field and hence a volumetric image. We have also proposed an intensity baseline correction method based on the partitioned projection, where the first and the second moments of pixel intensities at a patch in a simulated image are matched with those in a measured image via a linear transformation. The proposed method has been valid in simulated data and real phantom data. The algorithm is able to identify patches that contain relevant motion information, e.g. diaphragm region. It is found that intensity correction step is important to remove the systematic error in the motion prediction. For the simulation case, the sparse learning model reduced prediction error for the first PCA coefficient to 5%, compared to the 10% error when sparse learning is not used. 95th percentile error for the predicted motion vector is reduced from 2.40 mm to 0.92mm. In the phantom case, the predicted tumor motion trajectory is successfully reconstructed with 0.82 mm mean vector field error compared to 1.66 mm error without using the sparse learning method. The algorithm robustness with respect to sparse level, patch size, and existence of diaphragm, as well as computation time, has also been studied.
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